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    Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

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    Author
    Sofer, Tamar
    Zheng, Xiuwen
    Laurie, Cecelia A
    Gogarten, Stephanie M
    Brody, Jennifer A
    Conomos, Matthew P
    Bis, Joshua C
    Thornton, Timothy A
    Szpiro, Adam
    O'Connell, Jeffrey R
    Lange, Ethan M
    Gao, Yan
    Cupples, L Adrienne
    Psaty, Bruce M
    Rice, Kenneth M
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    Date
    2021-06-09
    Journal
    Nature Communications
    Publisher
    Springer Nature
    Type
    Article
    
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    See at
    https://doi.org/10.1038/s41467-021-23655-2
    Abstract
    In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.
    Keyword
    genetic association analyses
    phenotypic variance
    population stratification
    variant-specific inflation factors
    Whole Genome Sequencing
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/16016
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41467-021-23655-2
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